Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations

• URL: http://arxiv.org/abs/2403.02760v2
• Date: Tue, 12 Mar 2024 11:29:07 GMT
• Title: Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations
• Authors: Xiaonan Xu, Yichao Wu, Penghao Liang, Yuhang He, Han Wang
• Abstract summary: Large language models (LLMs) have superior capabilities in basic tasks of language understanding and generation. We introduce a representative approach to learning user and item representations using LLM as a feature encoder. We then reviewed the latest advances in LLMs techniques for collaborative filtering enhanced recommendation systems.
• Score: 19.405233437533713
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the boom of e-commerce and web applications, recommender systems have become an important part of our daily lives, providing personalized recommendations based on the user's preferences. Although deep neural networks (DNNs) have made significant progress in improving recommendation systems by simulating the interaction between users and items and incorporating their textual information, these DNN-based approaches still have some limitations, such as the difficulty of effectively understanding users' interests and capturing textual information. It is not possible to generalize to different seen/unseen recommendation scenarios and reason about their predictions. At the same time, the emergence of large language models (LLMs), represented by ChatGPT and GPT-4, has revolutionized the fields of natural language processing (NLP) and artificial intelligence (AI) due to their superior capabilities in the basic tasks of language understanding and generation, and their impressive generalization and reasoning capabilities. As a result, recent research has sought to harness the power of LLM to improve recommendation systems. Given the rapid development of this research direction in the field of recommendation systems, there is an urgent need for a systematic review of existing LLM-driven recommendation systems for researchers and practitioners in related fields to gain insight into. More specifically, we first introduced a representative approach to learning user and item representations using LLM as a feature encoder. We then reviewed the latest advances in LLMs techniques for collaborative filtering enhanced recommendation systems from the three paradigms of pre-training, fine-tuning, and prompting. Finally, we had a comprehensive discussion on the future direction of this emerging field.

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